A data-model-fusion prognostic framework for dynamic system state forecasting

Engineering Applications of Artificial Intelligence

Volumn 25, Issue 4, 2012, Pages 814-823

  Liu, J ^a   Wang, W ^b   Ma, F ^c   Yang, Y B ^d   Yang, C S ^e  

^a CARLETON UNIVERSITY   (Canada)

^b LAKEHEAD UNIVERSITY   (Canada)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d NANJING UNIVERSITY   (China)

^e NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

Failure prognostics; Fault diagnosis; Neural fuzzy systems; Neural networks; Nonlinear prediction; Remaining useful life prediction

Indexed keywords

APPLICATION EXAMPLES; BAYESIAN LEARNING; DATA-DRIVEN; DATA-DRIVEN METHODS; FORECASTING TOOLS; HISTORY DATA; LITHIUM-ION BATTERY; NEURAL FUZZY SYSTEMS; NONLINEAR PREDICTION; PARTICLE FILTERING; PREDICTION MODEL; REMAINING USEFUL LIFE PREDICTIONS; REMAINING USEFUL LIVES; STATE FORECASTING; SYSTEM DEGRADATION; SYSTEM EVOLUTION; SYSTEM STATE; UNKNOWN PARAMETERS;

DIAGNOSIS; ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY; FAILURE ANALYSIS; MATHEMATICAL MODELS; NEURAL NETWORKS; SIGNAL FILTERING AND PREDICTION; UNCERTAINTY ANALYSIS;

FORECASTING;

EID: 84862795692     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2012.02.015     Document Type: Article

References (36)

1. D.E. Adams Nonlinear damage models for diagnosis and prognosis in structural dynamic systems Proc. SPIE 4733 2002 180 191
2. A. Atiya, S. El-Shoura, S. Shaheen, and M. El-Sherif A comparison between neural-network forecasting techniques-case study: river flow forecasting IEEE Trans. Neural Networks 10 1999 402 409
3. D.R. Brillinger The identification of polynomial systems by means of higher order spectra J. Sound Vib. 12 1970 301 313
4. D. Chelidze, and J.P. Cusumano A dynamical systems approach to failure prognosis J. Vib. Acoust. 126 2004 2 8
5. Douc, R., Cappe, O., Moulines, E., 2005. Comparison of resampling schemes for particle filtering. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. pp. 64-69.
6. L. Eren, and M.J. Devaney Bearing damage detection via wavelet packet decomposition of the stator current IEEE Trans. Instrum. Meas. 53 2004 431 436
7. J.H. Friedman Multivariate adaptive regression splines Ann. Stat. 19 1991 1 141
8. J.H. Friedman, and W. Stuetzle Projection pursuit regression J. Am. Stat. Assoc. 76 1981 817 823
9. L. Gao, S. Liu, and R.A. Dougal Dynamic lithium-ion battery model for system simulation IEEE Trans. Compon. Packag. Technol. 25 2002 495 505
10. K. Goebel, B. Saha, A. Saxena, J.R. Celaya, and J.P. Christophersen Prognostics in battery health management IEEE Instrum. Meas. Mag. 11 2008 33 40
11. S. Gupta, and A. Ray Real-time fatigue life estimation in mechanical structures Meas. Sci. Technol. 18 2007 1947 1957
12. D. Husmeier Neural Networks for Conditional Probability Estimation: Forecasting beyond Point Prediction 1999 Springer London
13. J. Jang ANFIS: adaptive-network-based fuzzy inference system IEEE Trans. Syst. Man. Cybern. Part B 23 1993 665 685
14. A. Jardine, D. Lin, and D. Banjevic A review on machinery diagnostics and prognostics implementing condition-based maintenance Mech. Syst. Signal Process. 20 2006 1483 1510
15. J. Korbicz Fault Diagnosis: Models, Artificial Intelligence, Applications 2004 Springer Berlin
16. J.M. Lee, C.K. Yoo, and I.B. Lee Statistical process monitoring with independent component analysis J. Process Control 14 2004 467 485
17. Y. Liang, and X. Liang Improving signal prediction performance of neural networks through multi-resolution learning approach IEEE Trans. Syst. Man Cybern. Part B 36 2006 341 352
18. Liu, J., Saxena, A., Goebel, K., Saha, B., Wang, W., 2010. An adaptive recurrent neural network for remaining useful life prediction of lithium ion batteries. Proceedings of the Annual Conference of the Prognostics and Health Management Society, Portland, OR.
19. J. Liu, W. Wang, and F. Golnaraghi An extended wavelet spectrum for bearing fault diagnostics IEEE Trans. Instrum. Meas. 57 2008 2801 2812
20. J. Liu, W. Wang, and F. Golnaraghi A multi-step predictor with a variable input pattern for system state forecasting Mech. Syst. Signal Process. 2315 2009 86 99
21. J. Luo, A. Bixby, K. Pattipati, L. Qiao, M. Kawamoto, and S. Chigusa An interacting multiple model approach to model-based prognostics Syst. Security Assur. 1 2003 189 194
22. M. Pecht, and R. Jaai A prognostics and health management roadmap for information and electronics-rich systems Microelectron. Reliab. 50 2010 317 323
23. B. Saha, K. Goebel, S. Poll, and J. Christophersen Prognostics methods for battery health monitoring using a Bayesian framework IEEE Trans. Instrum. Meas. 58 2009 291 296
24. A. Saxena, J. Celaya, B. Saha, S. Saha, and K. Goebel Metrics for offline evaluation of prognostics performance Int. J. Progno. Health Manage. 1 2010 20 26
25. ∞, and Nonlinear Approaches 2006 Wiley-Interscience
26. R.T. Subba On the theory of bilinear time series models J. Roy. Stat. Soc. 43 1981 244 255
27. M.K. Tiwari, and C. Chatterjee Uncertainty assessment and ensemble flood forecasting using bootstrap based neural networks (BANNs) J. Hydrol. 38 2010 20 33
28. H. Tong, and K.S. Lim Threshold autoregression, limited cycles and cyclical data J. Roy. Stat. Soc. 42 1980 245 292
29. P. Tse, and D. Atherton Prediction of machine deterioration using vibration based fault trends and recurrent neural networks J. Vib. Acoust. 121 1999 355 362
30. E. Walter, and L. Pronzato Identification of Parametric Models from Experimental Data 1997 Springer Berlin
31. C. Wang, and R.X. Gao Wavelet transform with spectral post-processing for enhanced feature extraction IEEE Trans. Instrum. Meas. 52 2003 1296 1301
32. W. Wang, and J. Vrbanek An evolving fuzzy predictor for industrial applications IEEE Trans. Fuzzy Syst. 16 2008 1349 1439
33. S. Yagiz, C. Gokceoglu, E. Sezer, and S. Iplikci Application of two non-linear prediction tools to the estimation of tunnel boring machine performance Eng. Appl. Artif. Intell. 22 2009 808 814
34. J. Yu, and S.J. Qin Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models AIChE J. 54 2008 1811 1829
35. J. Yu, and S.J. Qin Multiway Gaussian mixture model based multiphase batch process monitoring Ind. Eng. Chem. Res. 48 2009 8585 8594
36. J. Yu Localized fisher discriminant analysis based complex chemical process monitoring AIChE J. 57 2011 1817 1828